1. Field of the Invention
The present invention generally relates to a computer implemented decision support system for determining a production schedule of feasible material releases within a complex multi-stage manufacturing system architecture.
2. Background Description
A key requirement for successful operations management in any manufacturing industry is the coordination of both available and future supply with both existing and future demand. This process is referred to as supply-chain management. Large-scale manufacturing systems, such as those encountered in semiconductor manufacturing, involve complex distributed supply/demand networks. These large-scale manufacturing systems are linked with suppliers and distribution channels worldwide and have global manufacturing networks comprising, for example, a single site to dozens of sites. Analyzing and coordinating internal manufacturing logistics with external links to suppliers and distribution channels is critical to optimizing material flows, managing product mix profiles, and evaluating manufacturing system design changes.
Semiconductor manufacturing is a complex and refined process involving everything from growing silicon crystals, the source of silicon wafers upon which integrated circuits are grown, to the actual placement and soldering of chips to a printed circuit board. Initially, raw wafers are cut from a silicon ingot and processed through a sequence of work centers. The end goal of this process is to build a set of integrated circuits on the surface of the silicon wafer according to a specific circuit design. This process involves repeatedly applying four basic steps: (i) deposition, (ii) photolithography, (iii) etching and (iv) ion implantation. These steps are the processes by which materials with specific dielectric properties (e.g., conductors, insulators) are deposited on the surface of the wafer according to the precise circuit design specifications. These processes are repeated many times to build up several layers (typically between 12 and 25) of the circuits.
Once the circuits have been built on the wafers they are tested to determine the resultant yield of operational circuits and tagged for reference. Circuits are then diced and sorted, and subsequently wire bonded to a substrate to assemble a module. These modules, which are further tested to determine electromagnetic and thermal characteristics, are eventually combined on printed circuit boards to make cards. Finally, the cards are tested and those that pass are eventually used in the assembly of a wide range of finished electronic products (e.g., PCs, printers, CD players, etc.). From the point of view of semiconductor manufacturing, the modules and cards are the finished products taken to market.
There are many aspects of extended enterprise supply chain planning (EESCP) systems which affect the generation of production plans. For example, a large number of different parts are required to produce finished products such as modules and cards. From the above example, producing a module requires several subcomponent items including, but not limited to, a silicon wafer, a wire lead frame, substrate and the like. Furthermore, production of a module requires that several different types of resources be available (e.g., work centers, personnel and the like) for operations such as dice, pick and sort, testing, etc. For a particular manufacturing plant it is often (but not always) the case that there is only one way to produce a particular part number (PN). However, at the EESCP level it is common for there to be multiple sources from which to obtain necessary material supply as well as multiple options for resources to use for production. These options for supply and capacity resources to meet demand create complex tradeoffs involving decisions such as:
which plant to source a particular part number (PN) from,
which process in a given plant to use to make a PN,
material substitutions, and
whether to build inventory, and so on.
At a macro level, the problem involves optimally balancing material flows across a supply/demand network given finite available capacity, geographically differentiated supply and demand locations, material processing costs, inventory holding costs, parametric data (e.g., product yields, cycle times, etc.) and the like.
The complicated process architecture in the semiconductor manufacturing industry creates unavoidably long lead times for processing through all manufacturing stages to produce finished products. These production lead times necessitate the advance planning of production so that material releases throughout the production system are coordinated with the end customers"" demand for any of a wide range of finished products (typically on the order of thousands in semiconductor manufacturing). Such advance planning depends on the availability of finite resources (finished goods inventory, work in process (WIP), workcenter capacity, etc.) and may tradeoff utilization of the resources at different locations, using different processes.
Planning and scheduling functions within the semiconductor manufacturing industry can be categorized in various ways. The following summary (Sullivan and Fordyce, xe2x80x9cIBM Burlington Logistics Management Systemsxe2x80x9d, Interfaces, 20, 1, 43-64, 1990), is based on a tier system in which each tier is defined by the time frame to which the decisions pertain.
Tier 1: Long range (3 months to 7 yr) strategic level decisions such as mergers, capacity acquisition, major process changes, new product development, and long term policy based decisions.
Tier 2: Medium range (1 week to 6 months) tactical scheduling involving yield and cycle time estimation, forecasting and demand management, material release planning and maintenance scheduling.
Tier 3: Short to medium range (weekly planning) operational scheduling for optimizing consumption and allocation of resources and output of product, demand prioritization techniques, capacity reservation and inventory replenishment.
Tier 4: Short range (daily) dispatch scheduling for addressing issues such as machine setups, lot expiration, prioritizing of late lots, job sequencing, absorbing unplanned maintenance requirements and assigning personnel to machines.
Due to the complicated process architecture and unavoidably long lead times to complete processing through all manufacturing stages for a finished product, advance planning decisions are a necessity. The above taxonomy of planning and scheduling decisions is hierarchical; that is, decisions in higher tiers affect decisions in the lower tiers. For example, long range capacity acquisition decisions determine eventual yield and cycle times, the available resources that can be utilized, and the extent to which maintenance needs to be scheduled in the future. Decisions in higher tiers, by the nature of their long time frames, are made under considerable uncertainty, and seek to anticipate future requirements based on current information. On the other hand, lower level tier decisions are of a corrective/reactive nature and act to absorb uncertainty not accounted for in the higher tiers. As an illustration, advanced production planning and scheduling decision support systems are typically run on a weekly basis, however, the planning horizon for such runs may range several years depending on the planning horizon of interest and the level of detail in forecasting. Thus, advance planning systems may impact decisions in tiers 1, 2 and 3 which, in turn, affect tier 4 decisions. Therefore, the matching of assets to demand at the EESCP level is a major planning activity which affects decisions within all tiers.
If unlimited assets were available, then the matching of demand with assets would be straightforward. In reality, however, finite supply and capacity create constraints on production scheduling. These constraints make the determination of a feasible production schedule (let alone an optimal one) a complex problem.
Major Production Planning Activities
The large-scale nature of production planning activities for EESCP as well as specialized processes specific to semiconductor manufacturing make production planning and scheduling a complex problem. The major activities in the production planning process are typically described in three categories: Supply Aggregation, Material Requirements Planning and Resource Allocation.
Supply Aggregation (SA):
SA involves capturing and transforming micro/factory floor details into a manageable data set. For example, work in progress (WIP) at a particular work center in the manufacturing system may ultimately travel through a variety of different routings depending on which type of finished product it is eventually used to produce. However, at any given point in the system a set of operations can be isolated which are common to all potential routing that the WIP can travel through from that point forward. In other words, a limited set of the immediate future operations required for the WIP are known. The purpose of the supply aggregation step is to project WIP forward through these required work centers to points at which decisions regarding alternative routings are necessary. As the WIP is moved forward its amount is adjusted for yield losses at the work centers it travels through. Furthermore, the time at which the WIP becomes available at the projected work center is computed based on known cycle times at each work center. Although these times should occur over a continuum, in practice they are discretized into a finite set of time periods. The end result of SA is to reduce the number of material release points considered to the level of resolution that is required for EESCP.
Material Requirements Planning (MRP)
MRP is a well known production scheduling method based on an explosion of finished product demand using manufacturing information such as the bill-of-material (BOM), yield and cycle times, inventory, and planned receipts. MRP is based on taking demand for finished product and sequentially computing the implied demand on the components used to manufacture the finished product. At each level of the BOM, required material releases are determined as well as the ideal release date based on cycle times at each work center.
For example, FIG. 1 shows a high level block diagram of the BOM for semiconductor manufacturing which can be broken into the following four aggregate stages: wafer stage 110, device/substrate stage 120, module stage 130, and card stage 140. These aggregate stages may involve many steps each of which may significantly impact the flow of materials through the manufacturing system. For example, the wafer stage 110 may involve wafer fabrication involving many passes through photolithography work centers to build multiple levels of a circuit structure. In the device substrate stage 120, the dicing of the silicon wafer involves a single item in the production process which is then converted into different devices. Also, the card stage 140 may involve the assembly of many devices to generate a single card. These stages result in multiple qualities of items being output from various stages of the manufacturing system according to a known distribution.
FIG. 2 demonstrates a simple BOM calculation for a rigid supply-chain with some additional complexity (as compared to FIG. 1). FIG. 2 further illustrates the effect of demand for some finished product xe2x80x9cAxe2x80x9d on the next level of MRP releases for components of xe2x80x9cAxe2x80x9d. In FIG. 2, component xe2x80x9cAxe2x80x9d is assembled from subassemblies B1 and B2 in differing ratios, 1:2 and 1:1, respectively. Thus, demand for 100 units of component xe2x80x9cAxe2x80x9d induces requirements for a material release of 200 units of B1 and 100 units of B2. These MRP releases, in turn, induce requirements for MRP releases of components of B1 and B2 (i.e., C1, C2 and C3).
Another important feature of MRP is the determination of release dates (also referred to as time phasing). This is performed by using manufacturing cycle times for each level of the BOM to work backwards from the finished product delivery date. For example, in FIG. 2, if one week is required to assemble component xe2x80x9cAxe2x80x9d, two weeks to assemble each of B1 and B2, and two weeks to build each of C1, C2 and C3, then releases for C1, C2 and C3 must be scheduled five weeks in advance of the delivery date for component xe2x80x9cAxe2x80x9d. Together the material release sizes and timing comprise the production schedule.
Resource Allocation (RA)
RA systematically adjusts the ideal set of releases generated by the MRP to make them feasible with respect to constraints on limited resources. Resources allocated can be separated into two groups: supply and capacity. The fundamental difference between these two types of assets is that unutilized supply is available to apply at later time periods whereas unutilized capacity is not available at a later time period.
Historically a broad group of methodologies, referred to as extended MRP or MRP II, include steps in which capacity requirements are evaluated based on releases generated by MRP. A method referred to as xe2x80x9cHeuristic Best Can Doxe2x80x9d (See, for example, U.S. Pat. No. 5,971,585) extends the capability of MRP II based systems from analysis to the actual development of a feasible near optimal production schedule. By example, when supply and/or capacity constraints are violated by the MRP releases, the schedule of releases is adjusted in time by moving a portion of the release to a sufficiently earlier period in time (if possible and later if necessary) such that the required supply and capacity are available. This involves moving up from lower to higher levels of the BOM (implosion) and allocating resources sequentially at each level based on a priority ranking of the MRP material releases which is performed through pegging in which priority ranking information is passed between levels of the BOM. That is, pegging refers to the passing and storing of information about the different demand implied on components from one level of the BOM to the next level of the BOM.
FIG. 3 shows an example of pegging with two finished products and three components for a two-level BOM. In FIG. 3, finished products xe2x80x9cAxe2x80x9d and xe2x80x9cBxe2x80x9d share component 2. Thus, implied demand for component 2 is subcategorized into demand of type xe2x80x9cAxe2x80x9d and type xe2x80x9cBxe2x80x9d.
Another option, instead of the use of a heuristic, is linear programming (LP) based technology. Such an approach is based on a mathematical formulation/definition of a problem and systematic determination of an optimal solution to the problem. (See, for example, U.S. Pat. No. 5,971,585). The LP based approach is often superior to heuristic based approaches, however, the LP approaches may involve significantly greater computation time and, in fact, problems may, in some cases, be effectively unsolvable.
Specialized process dependent factors in semiconductor manufacturing introduce additional complexities beyond those that can be readily handled by basic MRP. For example, at certain stages of the manufacturing process there is the opportunity for material substitution in which higher quality items are substituted for lower quality items. This opportunity presents itself due to a process known as binning whereby a distribution of product qualities (e.g., 1100 MHZ, 900 MHZ, 700 MHZ processors) is created for circuits built on a silicon wafer. Material substitution and binning are sources of additional complexity in semiconductor manufacturing that must be accounted for in computing an adequate production schedule.
In some simple cases the effect of binning is to link decisions about material releases between only two levels within the BOM. In more complex cases, substitution opportunities may result in decisions being made at a particular level of the BOM which potentially affect supply chain logistics at far removed parts of the BOM. Similar complex substitution decisions are necessary when there are multiple processes and plant locations by which a particular PN can be produced. In this case, decisions must be made about where to serve supply from rather than whether to substitute existing supply. When substitution decision affects are localized, heuristics provide adequate solutions; however, LP models are a better approach when the choice of an assembly to use to cover demand upstream in a supply chain affects the flow of material for all subassemblies, components, etc. throughout the supply chain.
FIG. 4 shows specialized processes in semiconductor manufacturing with a high level example of the module, device and wafer stages in semiconductor manufacturing. First, wafer xe2x80x9cWxe2x80x9d yields a distribution of devices A1, B1, C1 in the proportions of 50%, 20% and 30%, respectively, in level 2. In the example, wafer xe2x80x9cWxe2x80x9d yields 100 chips, thus, the yield is 50 of A1, 20 of B1, and 30 of C1. Next, within the device stage of level 3 there are device dependent yield losses. For example, from A1 to A2 there is a 10% loss (i.e. 90% yield). Finally, devices are incorporated into modules in level 4, a process in which there is the opportunity to substitute higher quality (faster) devices for lower quality (slower) devices. For example, in FIG. 4, the prime module in level 4 can substitute for either the fast module or slow module. Similarly, the fast module can substitute for the slow module (but not vice versa). These additional features of semiconductor manufacturing are important considerations in determining material releases.
Data/Modeling Requirements
There are several key pieces of data that are required for EESCP. One key source of data is the Bill of Material (BOM). The BOM is the source of data that specifies components used in the assembly of each particular PN produced within the manufacturing system. The BOM can be used to generate a graphical representation of the stages within a manufacturing process for each of the produced finished products. The BOM also plays an important role in defining the structure of the supply-chain. For example, in FIG. 1, the aggregate stages involve many steps each of which can significantly impact the flow of materials through the manufacturing system. Illustrative of this point is the wafer stage which involves wafer fabrication involving many passes through photolithography work centers to build multiple levels of circuit structure. Dicing of the silicon wafer involves a single item in the production process (wafer) being converted into many items (devices). Alternatively, the card stage may involve the assembly of many items (devices) to generate a single item (card).
Another aspect specific to semiconductor manufacturing is the fact that the quality of manufactured items varies as a natural result of the manufacturing process. This results in multiple qualities of items being output from various stages of the manufacturing system according to a known distribution. In addition to the information associated with the BOM, other sources of manufacturing information such as yields, cycle times, shipping routes, etc. are also important for EESCP. Other typical key sources of information may include:
various build options by process and plant location,
a yield and cycle time for PNs by process and plant location,
capacity availability by process and plant location,
capacity requirement rates for PNs by process and plant location,
inventory holding costs, processing costs, and backorder costs,
demand statement, and
binning distributions.
Many publications outlining MRP methodology have been published (see for example, xe2x80x9cMaterial Requirements Planningxe2x80x9d, by Joseph Orlicky, published by McGraw Hill, 1993). Plans generated using MRP are often referred to as ideal plans since they are uncapacitated and can be temporally infeasible (they assume unlimited supply and capacity). RA is concerned with the allocation of limited capacity and materials to generate a feasible production schedule. Various rough-cut capacity planning methods have been documented in the literature (see, for example, Silver and Peterson, 1998, xe2x80x9cInventory Management and Production Planning and Schedulingxe2x80x9d, 3rd edition, John Wiley and Sons). However, detailed capacitated material release scheduling has been much less explored. One such method is described in U.S. Pat. No. 5,971,585, assigned to the common assignee (International Business Machines, Corp.).
Furthermore, many other patents and known literature consider either LP based models or heuristics, alone. None of these models combine LP and heuristics models. Production planning problems in general can benefit from the use of either LP or heuristic based modeling approaches. There is a basic tradeoff, however, between the two different approaches. The heuristic approach has the advantage of being computationally fast and is capable of handling some of the specialized processes in semiconductor manufacturing. On the other hand, in some cases decisions about matching supply with demand at a particular part of the supply chain can significantly affect material flows in far removed parts of the supply chain. In such cases an LP model is best suited for the semiconductor manufacturing processes.
U.S. Pat. No. 5,943,484 includes the concept of automatically assigning part numbers to LP or Heuristic processing depending upon the complexity and connections of the part to other parts through the bills of material supply chain. However, U.S. Pat. No. 5,943,484 addresses the problem only in the context of an (advanced) MRP and also does not partition into distinct manufacturing stages.
Leachman et al., xe2x80x9cIMPReSS: An automated production-planning and delivery quotation system at Harris Corporation-Semiconductor sectorxe2x80x9d, Interfaces, 26, 1, pages 6-37, 1996, uses a decomposition approach to solve a large-scale semiconductor production planning problem. The Leachman et al. approach assumes a fixed or rigid bill of material structure; however, general process architectures (such as in the IBM Microelectonics semiconductor manufacturing process architecture) do not fit such specific BOM structure assumptions and therefore the Leachman et al. approach cannot be used as a production planning tool in such systems. By way of example, the Leachman et al. solution does not properly handle the double-speed sorting process, or any situations where the solution at one set of BOM levels depends upon the supply chain characteristics at other sets of BOM levels.
FIG. 5 provides an example of double speed sorting. In this example, wafers are processed in the level 1 and level 2 (front end and back end, respectively) of the line operations to produces devices (circuits that reside on a wafer) in level 3. The wafers are subsequently diced, and individual devices are tested. Such testing reveals, in level 4, that some devices have fast speeds, while others have medium and slow speeds. These three categories of devices are used to produce three types of modules in level 5 (Modules 1, 2 and 3). These modules are then speed-tested and sorted a second time into groups A, B and C in level 6. In level 7, the module groups A, B and C are applied to demand for finished modules X, Y and Z according to substitution rules.
An important function of any EESCP system is to align the matching of supply with demand attributes. A common approach is to assign each order a customer demand class priority which ranks the importance of allocating supply to such orders. In order to be consistent in this regard, it is necessary to pass this information back through the BOM when determining the implied (dependent) requirements for subassemblies, components, etc. due to finished product demand resulting from the order. This allows comparison of the importance of committing material supply and/or capacity relative to other orders requiring the same resources. The Leachman et al. solution, however, does not appear to pass demand attributes such as customer class from the top to the bottom of the BOM and thus cannot provide proper demand prioritization at the lower level partitions.
Also, with regard to the specific motivation and exploitation of the decomposition scheme, the Leachman et al. model only applies LP or Heuristics, but not both, at a given level of the bills of material. As such, this model does not capture the complexity associated with mixing multiple model types, such as the need for capacity partitioning. Furthermore, the Leachman et al. model does not take advantage of decomposition to introduce nonlinear modeling functions. Finally, the Leachman et al. solution does not partition capacity.
In one aspect of the present invention, a method is provided for computing a production plan for part numbers (PNs) throughout a bill of material supply chain. The method includes the steps of separating the bill of materials (BOM) into separate manufacturing stages and partitioning each manufacturing stage into a heuristic processing and a linear programming processing partition. PNs are then assigned to partitions based on manufacturing steps and solution methods (i.e., heuristics or linear programming). A Material Requirements Planning (MRP) production plan is calculated for the each manufacturing stage using either the heuristic processing or the linear programming processing by moving backwards through the stages with respect to the BOM. A best-can-do production plan is calculated using either the heuristic processing or the linear programming processing, depending on the partitioning step by moving forward through the stages with respect to the BOM. The MRP production solution information is prepared for passing recursively backward to a next manufacturing stage. Similarly, information is passed forward based on the best-can-do calculating step.
In embodiments, the partitioning of the each manufacturing stage is defined by a specific manufacturing stage with respect to the BOM. Also, a part associated with the PNs requires linear programming processing due to the part""s inherent complexity or a user specified demand. The method also includes imploding and exploding (from top to bottom) the BOM, as well as pegging. The MPR-type linear programming processing may run with a time horizon that begins in the past and further provides, preferably, feasible solutions. The method may further included mapping functions for demand attribute information against needs based on fields determined by the linear programming processing and determining which of the needs support which demand attribute information. The needs may further be mapped against assets supplying the needs or against end-item demands they are supporting.
In another aspect of the present invention a system is provided for computing a production plan for part numbers (PNs) throughout a bill of material supply chain. A machine readable medium containing code for computing a production plan for part numbers (PNs) throughout a bill of material supply chain is also provided which implements the steps of the present invention.